Symcap

---

Project Summary

This project aimed to look at the spatial variability of Symbiodinium clades C and D in the Kane’ohe Bay, O’ahu, Hawai’i population of Montipora capitata. We investigated the distribution of symbionts at scales ranging from location in the bay to location on an individual reef. We also looked at differences among reef types (fringing vs. patch), colony color morph and depth. Heterogeneous mixtures of symbiont clades, termed “symbiont community composition” were considered in the analysis for spatial patterns. By investigating spatial variability of Symbiodinium, we furthered the understanding of stress-response in Kane’ohe Bay.

Library Packages

setwd("~/Symcap")
library(data.table)
library(devtools)
library(plyr)
library(reshape2)
library(popbio)
library(RgoogleMaps)
library(plotrix)
library(zoo)

Import/Merge Field and qPCR Data

Coral_Data <- read.csv("Coral_Collection.csv")
Coral_Data$Depth..m. <- as.numeric(as.character(Coral_Data$Depth..m.))
source_url("https://raw.githubusercontent.com/jrcunning/steponeR/master/steponeR.R")
Mcap.plates <- list.files(path = "qPCR_data", pattern = "txt$", full.names = T)
Mcap <- steponeR(files = Mcap.plates, delim="\t",
                 target.ratios=c("C.D"), 
                 fluor.norm = list(C=2.26827, D=0), 
                 copy.number=list(C=33, D=3))
Mcap <- Mcap$result
Mcap <- Mcap[grep("+", Mcap$Sample.Name, fixed=T, invert = T), ]
Mcap <- Mcap[grep("NTC", Mcap$Sample.Name, fixed = T, invert = T), ]
Mcap <- Mcap[grep("PCT", Mcap$Sample.Name, fixed = T, invert = T), ]
colnames(Mcap)[which(colnames(Mcap)=="Sample.Name")] <- "Colony"
Mcap$fail <- ifelse(Mcap$C.reps < 2 & Mcap$D.reps < 2, TRUE, FALSE)
fails <- Mcap[Mcap$fail==TRUE, ]
Mcap <- Mcap[which(Mcap$fail==FALSE),]
Mcap$C.D[which(Mcap$C.reps<2)] <- -Inf
Mcap$C.D[which(Mcap$D.reps<2)] <- Inf
Mcap <- Mcap[with(Mcap, order(Colony)), ]
Mcap$propC <- Mcap$C.D / (Mcap$C.D+1)
Mcap$propD <- 1-Mcap$propC
Mcap$propD[which(Mcap$C.D==-Inf)] <-1
Mcap$propC[which(Mcap$C.D==-Inf)] <-0
Mcap$propD[which(Mcap$C.D==Inf)] <-0
Mcap$propC[which(Mcap$C.D==Inf)] <-1
Mcap$Dom <- ifelse(Mcap$propC>Mcap$propD, "C", "D")
Symcap<-merge(Coral_Data, Mcap, by="Colony", all=T)
Symcap <- Symcap[with(Symcap, order(Colony)), ]
Symcap$Mix <- factor(ifelse(Symcap$propC>Symcap$propD, ifelse(Symcap$propD!=0, "CD", "C"), ifelse(Symcap$propD>Symcap$propC, ifelse(Symcap$propC!=0, "DC", "D"), NA)), levels = c("C", "CD", "DC", "D"))

Adjust Depth by Mean Sea Level

JuneTide=read.csv("Station_1612480_tide_ht_20160601-20160630.csv")
JulyTide=read.csv("Station_1612480_tide_ht_20160701-20160731.csv")
Tide<-rbind(JuneTide, JulyTide)
Tide$Time <- as.POSIXct(Tide$TimeUTC, format="%Y-%m-%d %H:%M:%S", tz="UTC")
attributes(Tide$Time)$tzone <- "Pacific/Honolulu"
Symcap$Time2 <- as.POSIXct(paste(as.character(Symcap$Date), as.character(Symcap$Time)), format="%m/%d/%y %H:%M", tz="Pacific/Honolulu")
Symcap$Time=Symcap$Time2
Round6 <- function (times)  {
  x <- as.POSIXlt(times)
  mins <- x$min
  mult <- mins %/% 6
  remain <- mins %% 6
  if(remain > 3L) {
    mult <- mult + 1
  } else {
    x$min <- 6 * mult
  }
  x <- as.POSIXct(x)
  x
}
Symcap$Time.r <- Round6(Symcap$Time)
Tide$Time.r <- Tide$Time
merged<-merge(Symcap, Tide, by="Time.r", all.x=T)
merged$newDepth <- merged$Depth..m.- merged$TideHT

Coral Collection Map

KB <- c(21.46087401, -157.809907) 
KBMap <- GetMap(center = KB, zoom = 13, maptype = "satellite", SCALE = 2, GRAYSCALE = FALSE)
Latitude=aggregate(Latitude~Reef.ID, data=Symcap, FUN = mean)
Longitude=aggregate(Longitude~Reef.ID, data = Symcap, FUN=mean)
XY<-merge(Latitude, Longitude, by="Reef.ID", all=T)
newcoords <- LatLon2XY.centered(KBMap, XY$Latitude, XY$Longitude, zoom=13)
XY$X <- newcoords$newX
XY$Y <- newcoords$newY
PlotOnStaticMap(KBMap, XY$Latitude, XY$Longitude, col=153, pch=21, bg="#FF7F50", lwd=2)

Dominant Symbiont by Reef Area

Symcap$Reef.Area <- ifelse(Symcap$Reef.Area!="Top", yes = "Slope", no = "Top")
results=table(Symcap$Dom, Symcap$Reef.Area)
chisq.test(results)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  results
## X-squared = 136.26, df = 1, p-value < 2.2e-16

prop.table(results, margin = 2)

##    
##         Slope       Top
##   C 0.7767857 0.3294574
##   D 0.2232143 0.6705426

par(mar=c(4, 4, 2, 6))
barplot(prop.table(results, margin = 2), col = c("gray10", "gray100"), xlab = "Reef Area", ylab = "Proportion of Dominant Symbiont")
legend("topright", legend=c("C", "D"), fill=c("gray10", "gray100"), inset = c(-.2, 0), xpd = NA)

Dominant Symbiont by Color Morph

results=table(Symcap$Dom, Symcap$Color.Morph)
chisq.test(results)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  results
## X-squared = 164.96, df = 1, p-value < 2.2e-16

prop.table(results, margin = 2)

##    
##         Brown    Orange
##   C 0.8896321 0.4103194
##   D 0.1103679 0.5896806

par(mar=c(4, 4, 2, 6))
barplot(prop.table(results, margin = 2), col = c("gray10", "gray100"), xlab = "Color Morph", ylab = "Proportion of Dominant Symbiont")
legend("topright", legend=c("C", "D"), fill=c("gray10", "gray100"), inset = c(-.2, 0), xpd = NA)

Symbiont Community Composition by Color Morph

results=table(Symcap$Mix, Symcap$Color.Morph)
chisq.test(results)

## 
##  Pearson's Chi-squared test
## 
## data:  results
## X-squared = 167.44, df = 3, p-value < 2.2e-16

prop.table(results, margin = 2)

##     
##            Brown      Orange
##   C  0.762541806 0.361179361
##   CD 0.127090301 0.049140049
##   DC 0.107023411 0.570024570
##   D  0.003344482 0.019656020

par(mar=c(4, 4, 2, 6))
barplot(prop.table(results, margin = 2), col = c("gray10", "gray50", "gray85", "gray100"), xlab = "Color Morph", ylab = "Symbiont Community Composition")
legend("topright", legend=c("C", "CD", "DC", "D"), fill=c("gray10", "gray50", "gray85", "gray100"), inset = c(-.2, 0), xpd = NA)

Symbiont Community Composition by Reef Area

Symcap$Reef.Area <- ifelse(Symcap$Reef.Area!="Top", yes = "Slope", no = "Top")
results=table(Symcap$Mix, Symcap$Reef.Area)
chisq.test(results)

## 
##  Pearson's Chi-squared test
## 
## data:  results
## X-squared = 138.97, df = 3, p-value < 2.2e-16

prop.table(results, margin = 2)

##     
##            Slope         Top
##   C  0.678571429 0.275193798
##   CD 0.098214286 0.054263566
##   DC 0.214285714 0.651162791
##   D  0.008928571 0.019379845

par(mar=c(4, 4, 2, 6))
barplot(prop.table(results, margin = 2), col = c("gray10", "gray50", "gray85", "gray100"), xlab = "Reef Area", ylab = "Symbiont Community Composition")
legend("topright", legend=c("C", "CD", "DC", "D"), fill=c("gray10", "gray50", "gray85", "gray100"), inset = c(-.2, 0), xpd = NA)

Color Morph by Reef Area

Symcap$Reef.Area <- ifelse(Symcap$Reef.Area!="Top", yes = "Slope", no = "Top")
results=table(Symcap$Color.Morph, Symcap$Reef.Area)
chisq.test(results)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  results
## X-squared = 81.109, df = 1, p-value < 2.2e-16

prop.table(results, margin = 2)

##         
##              Slope       Top
##   Brown  0.5523385 0.2015504
##   Orange 0.4476615 0.7984496

par(mar=c(4, 4, 2, 6))
barplot(prop.table(results, margin = 2), col = c("gray10", "gray100"), xlab = "Reef Area", ylab = "Color Morph")
legend("topright", legend=c("Brown", "Orange"), fill=c("gray10", "gray100"), inset = c(-.2, 0), xpd = NA)

Dominant Symbiont by Depth

merged$Dominant <- ifelse(merged$Dom=="C", 1, 0)
Dom1 <- subset(merged, !is.na(newDepth) & !is.na(Dominant))
results=glm(Dominant~newDepth, family = "binomial", data = merged)
anova(results, test = "Chisq")

## Analysis of Deviance Table
## 
## Model: binomial, link: logit
## 
## Response: Dominant
## 
## Terms added sequentially (first to last)
## 
## 
##          Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                       589     802.22              
## newDepth  1   97.617       588     704.61 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

logi.hist.plot(Dom1$newDepth, Dom1$Dominant, boxp = FALSE, type = "hist", col="gray", xlabel = "Depth (m)", ylabel = "", ylabel2 = "")
mtext(side = 4, text = "Frequency", line = 3, cex=1)
mtext(side = 4, text = "C                                       D", line = 2, cex = 0.75)
mtext(side = 2, text = "Probability of clade C Symbiont", line = 3, cex = 1)

Color Morph by Depth

merged$Color <- ifelse(merged$Color.Morph=="Orange", 1, 0)
results=glm(Color~newDepth, family = "binomial", data = merged)
anova(results, test = "Chisq")

## Analysis of Deviance Table
## 
## Model: binomial, link: logit
## 
## Response: Color
## 
## Terms added sequentially (first to last)
## 
## 
##          Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                       589     784.37              
## newDepth  1   26.708       588     757.67 2.366e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Color <- subset(merged, !is.na(newDepth) & !is.na(Color))
logi.hist.plot(independ = Color$newDepth, depend = Color$Color, type = "hist", boxp = FALSE, ylabel = "", col="gray", ylabel2 = "", xlabel = "Depth (m)")
mtext(side = 4, text = "Frequency", line = 3, cex=1)
mtext(side = 4, text = "Brown                                Orange", line = 2, cex = 0.75)
mtext(side = 2, text = "Probability of Orange Color Morph", line = 3, cex = 1)